Analytical trajectory model of air pollution

Summary The basic principles of the method of calculating air pollution in a complex terrain are presented. The method is based on a trajectory air pollution model. The formula for the distribution of pollutant concentration in a puff is obtained by solving a simple turbulent diffusion equation analytically. An example of the model's application is given.     

Fuzzy robust process capability indices for risk assessment of air pollution

Air pollution is one of the most important threats for the humanity. It can damage not only human health but also Earth’s ecosystem. Because of the harmful effects of air pollution, it should be controlled very carefully. To do the risk assessment of air pollution in Istanbul, the process capability indices (PCIs) which are very effective statistics to summarize the performance of process are used in this paper. Fuzzy PCIs are used to determine the levels of the air pollutants which are measured in different nine stations in Istanbul. Robust PCIs (RPCIs) are used when air pollutants have correlation. Fuzzy set theory has been applied for both PCIs and RPCIs to have more sensitive results. More flexible PCIs obtained by using fuzzy specification limits and fuzzy standard deviation are used to evaluate the air pollution’s level of Istanbul. Additionally some evaluation criteria have been constructed for fuzzy PCIs to interpret the air pollution.     

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

High concentrations of air pollutants in the ambient environment can result in breathing problems with human communities. Effective assessment of health-impact risk from air pollution is important for supporting decisions of the related detection, prevention, and correction efforts. However, the quality of information available for environmental/health risk assessment is often not satisfactory enough to be presented as deterministic numbers. Stochastic method is one of the methods for tackling those uncertainties, by which uncertain information can be presented as probability distributions. However, if the uncertainties can not be presented as probabilities, they can then be handled through fuzzy membership functions. In this study, an integrated fuzzy-stochastic modeling (IFSM) approach is developed for assessing air pollution impacts towards asthma susceptibility. This development is based on Monte Carlo simulation for the fate of SO₂ in the ambient environment, examination of SO₂ concentrations based on the simulation results, quantification of evaluation criteria using fuzzy membership functions, and risk assessment based on the combined fuzzy-stochastic information. The IFSM entails (a) simulation for the fate of pollutants in ambient environment, with the consideration of source/medium uncertainties, (b) formulation of fuzzy air quality management criteria under uncertain human-exposure pathways, exposure dynamics, and SPG-response variations, and (c) integrated risk assessment under complexities of the combined fuzzy/stochastic inputs of contamination level and health effect (i.e., asthma susceptibility). The developed IFSM is applied to a study of regional air quality management. Reasonable results have been generated, which are useful for evaluating health risks from air pollution. They also provide support for regional environmental management and urban planning.     

A mathematical formulation for optimal control of air pollution

In recent years the urban air pollutions caused by emissions from industry, transportation and energy consuming for daily living are getting worse in many major cities of China. Peoples awareness on air quality is increasing. Routine monitoring and foreca…     

A finite element model for the diffusion—convection equation with applications to air pollution problems

A method of solution for the diffusion-convection equation in one spatial dimension has been developed previously. The generalization of this method into two-spatial dimensions will be presented. The method employs space-time volume elements with edges joining the nodes at subsequent time levels oriented along the characteristics of the associated pure convection problem. The accuracy and utility of the method are demonstrated by solving several examples and results are compared with the exact solutions.     

spatialCopula: A Matlab toolbox for copula-based spatial analysis

The spatialCopula toolbox contains a set of Matlab functions that provides utilities for copula-based analysis of spatially referenced data, a topic which has re cently attracted much attention in spatial statistics. These tools have been developed to support the work flow in parameter estimation, spatial interpolation and visualization. They offer flexible and user-friendly software for dealing with non-Gaussian and extreme value data that possibly contain a spatial trend or geometric anisotropy. The objective of this paper is to give an introduction to the concept behind the software and to outline the functionality of the toolbox. We illustrate its usefulness by analyzing a data set here referred to as the Gomel data set, which includes moderately skewed radioactivity measurements in the region of Gomel, Belarus. The source codes are freely available in Matlab language on the author’s website (fam.tuwien.ac.at/~hakazian/software.html).     

Designing a monitoring network for detecting groundwater pollution with stochastic simulation and a cost model

A method is presented to design monitoring networks for detecting groundwater pollution at industrial sites. The goal is to detect the pollution at some distance from the site’s boundary so that it can be cleaned up or hydrologically contained before contaminating groundwater outside the site. It is assumed that pollution may occur anywhere on the site, that transport is by advection only and that no retardation and chemical reactions take place. However, the approach can be easily extended to include designated (and uncertain) source areas, dispersion and reactive transport. The method starts from the premise that it is impossible to detect 100% of all the contaminant plumes with reasonable costs and therefore seeks a balance between the risk of pollution and network density. The design approach takes account of uncertainty in the flow field by simulating realisations of conductivity, groundwater head and associated flow fields, using geostatistical simulation and a groundwater flow model. The realisations are conditioned to conductivity and head observations that may already be present on the site. The result is an ensemble of flow fields that is further analysed using a particle track program. From this the probability of missing a contaminant plume originating anywhere on the terrain can be estimated for a given network. From this probability follows the risk, i.e. the expected costs of an undetected pollution. The total costs of the monitoring strategy are calculated by adding the risk of pollution to the costs of installing and maintaining the monitoring wells and the routinely performed chemical analyses. By repeating this procedure for networks of varying well numbers, the best network is chosen as the one that minimises total cost. The method is illustrated with a simulated example showing the added worth of exploratory wells for characterising hydraulic conductivity of a site.     

Extension of observed flood series by combining a distributed hydro-meteorological model and a copula-based model

Long flood series are required to accurately estimate flood quantiles associated with high return periods, in order to design and assess the risk in hydraulic structures such as dams. However, observed flood series are commonly short. Flood series can be extended through hydro-meteorological modelling, yet the computational effort can be very demanding in case of a distributed model with a short time step is considered to obtain an accurate flood hydrograph characterisation. Statistical models can also be used, where the copula approach is spreading for performing multivariate flood frequency analyses. Nevertheless, the selection of the copula to characterise the dependence structure of short data series involves a large uncertainty. In the present study, a methodology to extend flood series by combining both approaches is introduced. First, the minimum number of flood hydrographs required to be simulated by a spatially distributed hydro-meteorological model is identified in terms of the uncertainty of quantile estimates obtained by both copula and marginal distributions. Second, a large synthetic sample is generated by a bivariate copula-based model, reducing the computation time required by the hydro-meteorological model. The hydro-meteorological modelling chain consists of the RainSim stochastic rainfall generator and the Real-time Interactive Basin Simulator (RIBS) rainfall-runoff model. The proposed procedure is applied to a case study in Spain. As a result, a large synthetic sample of peak-volume pairs is stochastically generated, keeping the statistical properties of the simulated series generated by the hydro-meteorological model. This method reduces the computation time consumed. The extended sample, consisting of the joint simulated and synthetic sample, can be used for improving flood risk assessment studies.     

混合ADI-FDTD亚网格技术在探地雷达频散媒质中的高效正演

提出混合ADI-FDTD亚网格技术开展频散介质GPR正演,即在物性参数变化剧烈局部区域采用细网格剖分ADI-FDTD计算,其他的区域采用粗网格剖分常规FDTD计算,ADI-FDTD突破了CFL条件的限制,可选取与粗网格一致的大时间步长,有效地提高了计算效率.本文首先基于Debye方程,推导了粗网格FDTD及细网格ADI-FDTD频散介质差分格式,着重对粗细两种网格结合的场值交换方式进行了深入探讨,给出了该算法的计算流程.然后以一个薄层模型为例,分别应用粗网格、细网格、混合ADI-FDTD亚网格算法对该模型进行正演,计算资源的占用及模拟精度说明了混合ADI-FDTD亚网格算法的优势.最后,建立频散介质与非频散介质的组合模型,应用3种方法对该模型进行正演,对比3种方法优劣,分析雷达剖面中非频散介质及频散介质中波形特征,有效地指导雷达资料的精确解释.     

A model of a homogeneous isotropic turbulent flow and its application for the simulation of cloud drop tracks

Abstract

A model of a homogeneous isotropic turbulent flow is presented. The model provides different realizations of the random velocity field component with given correlation latitudinal and lateral functions and a spatial structure which obeys the Kolmogorov theory of homogeneous and isotropic turbulence. For the generation of the turbulent flow the structural function of the flow in the form suggested by Batchelor (Monin and Yaglom, 1975) was used. This function describes the spectrum of turbulence both in the viscous and inertial ranges. The isotropy and homogeneity of the velocity field of the model are demonstrated.

The model is aimed at simulating the ‘‘fine'’ features of drop's (aerosol particles') motion, such as the deviations of drops’ velocity from the velocity of the flow, detailed structures of drops’ tracks, related to drops’ (particles') inertia. The model is intended also for the purpose of studying cloud drops’ and aerosol particles’ motion and their diffusional spreading utilizing the Monte Carlo methods.

Some examples of drop tracks for drops of different size are presented. Drops’ tracks are very sophisticated, so that the relative position of drops falling initially from the same point can vary drastically. In some cases drops’ tracks diverge very quickly, in other cases all drops move within a turbulent eddy along nearly the same closed tracks, but with different speed. The concentration of drop tracks along isolated paths is found in spite of the existence of a large number of velocity harmonics. It is shown that drops (aerosol particles) tend to leave some areas of the turbulent flow apparently due to their inertia. These effects can possibly contribute to inhomogeneity of drops’ concentration in clouds at different spatial scales.     

A stochastic model for air injection into saturated porous media

Air injection into porous media is investigated by laboratory experiments and numerical modelling. Typical applications of air injection into a granular bed are aerated bio-filters and air sparging of aquifers. The first stage of the dynamic process consists of air injection into a fixed or a quasi-fixed water-saturated granular bed. Later stages could include stages of movable beds as well, but are not further investigated here. A series of laboratory experiments were conducted in a two-dimensional box of the size 60 cm × 38 cm × 0.55 cm consisting of glass walls and using glass beads of diameter 0.4–0.6 mm as granular material. The development of the air flow pattern was optically observed and registered using a digital video camera. The resulting transient air flow pattern can be characterized as channelled flow in a fixed porous medium with dynamic tree-like evolution behaviour. Attempts are undertaken to model the air injection process. Multiphase pore-scale modelling is currently disregarded since it is restricted to very small scales. Invasion percolation models taking into account gravity effects are usually restricted to slow processes. On the other hand a continuum-type two-phase flow modelling approach is not able to simulate the observed air flow pattern. Instead a stochastic continuum-type approach is discussed here, which incorporates pore-scale features on a subscale, relevant for the immiscible processes involved. Consequently, the physical process can be modelled in a stochastic manner only, where the single experiment represents one of many possible realizations. However, the present procedure retains realistic water and air saturation patterns and therefore produces similar finger lengths and widths as observed in the experiments. Monte Carlo type modelling leads to ensemble mean water saturation and the related variance.     

A truncated inverted beta distribution with application to air pollution data

The inverted beta distributions are popular models for hydrology. However, they suffer from the fact that they do not possess finite moments of all orders. In this note, a truncated version of the inverted beta distribution is introduced, which possesses finite moments of all orders and could therefore be a better model for hydrological data with a finite upper bound. Explicit expressions for the moments of the truncated distribution and its estimation procedure are derived. An application is provided to ozone level data from New York.     

A multivariate Bernstein copula model for permeability stochastic simulation

This paper introduces a general nonparametric method for joint stochastic simulation of petrophysical properties using the Bernstein copula. This method consists basically in generating stochastic simulations of a given petrophysical property (primary variable) modeling the underlying empirical dependence with other petrophysical properties (secondary variables) while reproducing the spatial dependence of the first one.This multivariate approach provides a very flexible tool to model the complex dependence relationships of petrophysical properties. It has several advantages over other traditional methods, since it is not restricted to the case of linear dependence among variables, it does not require the assumption of normality and/or existence of moments.In this paper this method is applied to simulate rock permeability using Vugular Porosity and Shear Wave Velocity (S-Waves) as covariates in a carbonate double-porosity formation at well log scale. Simulated permeability values show a high degree of accuracy compared to the actual values.     

Development and testing of numerical methods for two-way nested air pollution modelling

A new 3-D model REGINA (REGIonal high resolutioN Air pollution model) is under development at the National Environmental Research Institute (NERI). The model is based on pieces from several models developed over the last decades at NERI. The aim of the work is to develop a nested model which can operate with very high resolution in both space and time. To fulfill this aim the choice and implementation of accurate numerical methods is crucial. The model will be applied for studying air pollution phenomena (both monitoring, forecasting and scenarios) over Denmark. The present paper is focussed on the modification, implementation and testing of a numerical method for treating the horizontal advection in the model as well as the implementation of two-way nesting techniques. The horizontal transport in the model is solved using an accurate space derivative algorithm. This method is traditionally implemented with periodic boundary conditions, however, this is not an option for nested modelling. A new method for calculating non-periodic boundary conditions has been developed in order to overcome this problem. Extensive testing of the numerical solution of the advection and the coupling of the solution of advection and chemistry in the model using Molenkamp–Crowley rotation tests have been carried out. The results show that the model with the current implementation of numerical methods is suitable for calculating air pollution levels with high resolution.     

A computationally efficient method for uncertainty analysis of SWAT model simulations

The physically based distributed hydrological models are ideal for hydrological simulations; however most of such models do not use the basic equations pertaining to mass, energy and momentum conservation, to represent the physics of the process. This is plausibly due to the lack of complete understanding of the hydrological process. The soil and water assessment tool (SWAT) is one such widely accepted semi-distributed, conceptual hydrological model used for water resources planning. However, the over-parameterization, difficulty in its calibration process and the uncertainty associated with predictions make its applications skeptical. This study considers assessing the predictive uncertainty associated with distributed hydrological models. The existing methods for uncertainty estimation demand high computational time and therefore make them challenging to apply on complex hydrological models. The proposed approach employs the concepts of generalized likelihood uncertainty estimation (GLUE) in an iterative procedure by starting with an assumed prior probability distribution of parameters, and by using mutual information (MI) index for sampling the behavioral parameter set. The distributions are conditioned on the observed information through successive cycles of simulations. During each cycle of simulation, MI is used in conjunction with Markov Chain Monte Carlo procedure to sample the parameter sets so as to increase the number of behavioral sets, which in turn helps reduce the number of cycles/simulations for the analysis. The method is demonstrated through a case study of SWAT model in Illinois River basin in the USA. A comparison of the proposed method with GLUE indicates that the computational requirement of uncertainty analysis is considerably reduced in the proposed approach. It is also noted that the model prediction band, derived using the proposed method, is more effective compared to that derived using the other methods considered in this study.     

Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on GPS tracks

In this work, we address the mismatch in spatio-temporal resolution between individual, point-location based exposure and grid cell based air quality model predictions by disaggregating the grid model results. Variability of PM₁₀ point measurements was modelled within each grid cell by the exponential variogram, using point support concentration measurements. Variogram parameters were estimated over the study area globally using constant estimates, and locally by multiple regression models using traffic, weather and land use data. Model predictions of spatio-temporal variability were used for geostatistical unconditional simulation, estimating the deviation of point values from grid cell averages on GPS tracks. The distribution of deviations can be used as an estimate of uncertainty for individual exposure. Results showed a relevant impact of the disaggregation uncertainties compared to other uncertainty sources, dependent of the model used for spatio-temporal variability. Depending on individual behaviour and variability of the pollutant, these uncertainties average out again over time.     

The gutenberg-richter law for earthquakes in air pollution episodes: A case study for Athens, Greece

The main aim of this study is to investigate whether the main components of the photochemical smog episodes over megacities obey the Gaussian distribution or do they follow the distribution of the Gutenberg-Richter law. To this end, a case study has been implemented for Athens, Greece, which is among the most densely populated capitals in the middle latitudes of the Northern Hemisphere. The data employed are hourly mean values of surface ozone and nitrogen dioxide concentrations collected by the National Air Pollution Monitoring Network during the period 1988–2008. The results obtained show that the surface ozone and nitrogen dioxide concentrations obey the Gutenberg-Richter law, while their extreme values follow the Generalized Pareto distribution. This finding is important for current efforts to reliably forecast the air pollutants concentrations and to quantify their contribution to climate change. Finally, the plausible mechanisms involved in air pollution dynamics leading to the above-mentioned behaviour are also discussed, assuming the air pollution system governed by non-linear processes.     

A framework for a seismic risk model for Greater Cairo

Following the adverse effects caused by the moderate M_s 5.4 event of October 1992, the need to model the risk from earthquakes occurring in or near Cairo was shown to be an essential tool to offset this threat in the future. To provide the necessary elements for a risk model, this paper describes a methodology for developing a ground-shaking model as well as an inventory database for the city. In the first part, a scheme is followed to integrate data on geological structures, seismic sources, seismicity and surface soil conditions to build-up an event-based hazard model. In the second part, a brief review of the history of seismic provisions in Egyptian codes is presented, and a detailed assessment of local maps and information is supplemented by results from street surveys to obtain building stock data and geographical resolutions. On the basis of these studies, the city is divided into a number of census-tracts, or geo-codes, of classified building and soil characteristics, representing a fundamental step towards the development of a full loss model.